Dataset Information

Fast covariance estimation for sparse functional data.

ABSTRACT: Smoothing of noisy sample covariances is an important component in functional data analysis. We propose a novel covariance smoothing method based on penalized splines and associated software. The proposed method is a bivariate spline smoother that is designed for covariance smoothing and can be used for sparse functional or longitudinal data. We propose a fast algorithm for covariance smoothing using leave-one-subject-out cross-validation. Our simulations show that the proposed method compares favorably against several commonly used methods. The method is applied to a study of child growth led by one of coauthors and to a public dataset of longitudinal CD4 counts.

SUBMITTER: Xiao L

PROVIDER: S-EPMC5807553 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Fast covariance estimation for sparse functional data.

Xiao Luo L Li Cai C Checkley William W Crainiceanu Ciprian C

Statistics and computing 20170411 3

Smoothing of noisy sample covariances is an important component in functional data analysis. We propose a novel covariance smoothing method based on penalized splines and associated software. The proposed method is a bivariate spline smoother that is designed for covariance smoothing and can be used for sparse functional or longitudinal data. We propose a fast algorithm for covariance smoothing using leave-one-subject-out cross-validation. Our simulations show that the proposed method compares f ...[more]

PMID: 29449762

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Dataset Information

Fast covariance estimation for sparse functional data.

Publications

Fast covariance estimation for sparse functional data.

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